CLI Input Analyzer Clustering Model for Security Detection
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Solution Overview
Problem
Conventional security management systems in data centers, such as AppDefense, face challenges in accurately detecting unwanted behavior in virtual machines due to simple string comparisons of command line interface (CLI) inputs, which can lead to false positives and negatives, especially when IP addresses change, despite both being associated with internal servers.
Innovation Solution
A CLI input analyzer is developed to create and train a clustering model for each process using a pool of normal CLI inputs, extracting features like length, entropy, and n-grams, and applying clustering algorithms to differentiate between normal and unwanted behaviors, with the option to retrain based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple string comparison is used to evaluate CLI inputs, then the security management system can operate quickly and simply, but it produces false positives and false negatives when IP addresses or other parameters change
Solution Approach 1:
The patent transforms the CLI input evaluation from simple string comparison to feature-based analysis by extracting parameters such as function name, number of parameters, parameter types, and n-grams. This allows the system to evaluate semantic meaning rather than exact string matches, resolving the contradiction between detection accuracy and system complexity by changing the evaluation parameters from raw strings to structured features
Solution Approach 2:
The patent replaces the mechanical string comparison mechanism with a machine learning-based clustering model. Instead of directly comparing CLI input strings, the system uses trained models that analyze extracted features and determine whether inputs represent normal or unwanted behavior, substituting a simple mechanical operation with an intelligent system that achieves higher accuracy
2Reliability
If deeper analysis is performed on execution code and network behavior, then threat detection capability is improved, but the overall security management system becomes more complex and resource-intensive
Solution Approach 1:
The patent segments the CLI input analysis into distinct feature extraction components (function name, parameter count, parameter types, n-grams) that can be independently processed. This segmentation allows the system to perform comprehensive analysis without overwhelming complexity, as each feature can be extracted and evaluated separately by the clustering model
Solution Approach 2:
The patent introduces feature extraction as an intermediary layer between the raw CLI input and the clustering model evaluation. This intermediary transforms complex CLI inputs into standardized features, making the analysis process more manageable and reducing the direct complexity between raw data and detection logic
Data Source
AI summary
Certain aspects of the present disclosure relate to methods and systems for evaluating a first command line interface (CLI) input of a process. The method comprises examining the first CLI input and selecting a first clustering model corresponding to the process, wherein the first clustering model is created based on a first clustering configuration and a first feature type combination. The method further comprises creating a first feature combination for the first CLI input based on the first feature type combination, evaluating the first CLI input using the first clustering model and the first feature combination, wherein the evaluating further comprises determining a similarity score corresponding to a similarity between the first feature combination and the one or more clusters, and determining whether or not the first CLI input corresponds to normal behavior based on the similarity score.